Detector Understanding with First LHC Data DESY, Hamburg, 29 June – 3 July 2009 Introduction To Jet & Missing Transverse Energy Reconstruction at LHC Peter.

Detector Understanding with First LHC Data DESY, Hamburg, 29 June – 3 July 2009 Introduction To Jet & Missing Transverse Energy Reconstruction at LHC Peter Loch University of Arizona Tucson, Arizona USA Peter Loch University of Arizona Tucson, Arizona USA

2 P. Loch U of Arizona June 30, 2009 Preliminaries Scope Scope Focus on explanation of features, algorithms and methods Focus on explanation of features, algorithms and methods Including some pointers to underlying principles, motivations, and expectations Including some pointers to underlying principles, motivations, and expectations Some reference to physics Some reference to physics Everything is based on simulations Everything is based on simulations Experiments may tell a (very?) different story in some cases Experiments may tell a (very?) different story in some cases Restricted to published material (mixed CMS/ATLAS audience) Restricted to published material (mixed CMS/ATLAS audience) i.e., we think we know more!! To the audience To the audience Clearly introductory character Clearly introductory character Please – ask questions! Please – ask questions! And please, do not try to compare ATLAS and CMS performances in detail from the plots shown here! And please, do not try to compare ATLAS and CMS performances in detail from the plots shown here! Hopefully useful even for people already working on the topics Hopefully useful even for people already working on the topics If you are bored – that’s what you get when you ask a research scientist to do this! If you are bored – that’s what you get when you ask a research scientist to do this!

3 P. Loch U of Arizona June 30, 2009 Roadmap 1.Jets and Missing Et at LHC a.Where do they come from? Selected hadronic final states Selected hadronic final states b.Physics collision environment Underlying event and pile-up Underlying event and pile-up 2.Jet reconstruction in general a.Jet algorithms at LHC Guidelines Guidelines Fixed cone Fixed cone Recursive recombination Recursive recombination Jet definition Jet definition b.Experimentalist’s view on jets Understanding of the object Understanding of the object Jet reconstruction task overview Jet reconstruction task overview 3.Jet measurement a.General signal features Response issues in non- compensating calorimeters Response issues in non- compensating calorimeters b.Detector signals Towers (ATLAS & CMS) Towers (ATLAS & CMS) Energy flow (CMS) Energy flow (CMS) Topological clusters (ATLAS) Topological clusters (ATLAS) c.Jet calibration Simulation based approach Simulation based approach Data driven Data driven 4.Jet reconstruction performance a.QCD di-jets Performance evaluation Performance evaluation b.Photon/z + jet(s) c.W mass spectroscopy 5.Missing transverse energy a.Event variables composition Understanding the variable Understanding the variable CMS & ATLAS approach CMS & ATLAS approach b.Scale and resolution Instrumental effects Instrumental effects Validation or calibration? Validation or calibration? c.Performance evaluation Simulation based Simulation based Data driven Data driven 6.Conclusions

4 P. Loch U of Arizona June 30, 2009 1. Jets & Missing Et at LHC 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

5 P. Loch U of Arizona June 30, 2009 Where do Jets come from at LHC? inclusive jet cross-section Fragmentation of gluons and (light) quarks in QCD scattering Fragmentation of gluons and (light) quarks in QCD scattering Most often observed interaction at LHC Most often observed interaction at LHC Decay of heavy Standard Model (SM) particles Decay of heavy Standard Model (SM) particles Prominent example: Prominent example: Associated with particle production in Vector Boson Fusion (VBF) Associated with particle production in Vector Boson Fusion (VBF) E.g., Higgs E.g., Higgs Decay of Beyond Standard Model (BSM) particles Decay of Beyond Standard Model (BSM) particles E.g., SUSY E.g., SUSY 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

6 P. Loch U of Arizona June 30, 2009 top mass reconstruction Fragmentation of gluons and (light) quarks in QCD scattering Fragmentation of gluons and (light) quarks in QCD scattering Most often observed interaction at LHC Most often observed interaction at LHC Decay of heavy Standard Model (SM) particles Decay of heavy Standard Model (SM) particles Prominent example: Prominent example: Associated with particle production in Vector Boson Fusion (VBF) Associated with particle production in Vector Boson Fusion (VBF) E.g., Higgs E.g., Higgs Decay of Beyond Standard Model (BSM) particles Decay of Beyond Standard Model (BSM) particles E.g., SUSY E.g., SUSY Where do Jets come from at LHC? 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c CERN-OPEN-2008- 020

7 P. Loch U of Arizona June 30, 2009 Fragmentation of gluons and (light) quarks in QCD scattering Fragmentation of gluons and (light) quarks in QCD scattering Most often observed interaction at LHC Most often observed interaction at LHC Decay of heavy Standard Model (SM) particles Decay of heavy Standard Model (SM) particles Prominent example: Prominent example: Associated with particle production in Vector Boson Fusion (VBF) Associated with particle production in Vector Boson Fusion (VBF) E.g., Higgs E.g., Higgs Decay of Beyond Standard Model (BSM) particles Decay of Beyond Standard Model (BSM) particles E.g., SUSY E.g., SUSY Where do Jets come from at LHC? 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c CERN-OPEN-2008- 020

8 P. Loch U of Arizona June 30, 2009 Fragmentation of gluons and (light) quarks in QCD scattering Fragmentation of gluons and (light) quarks in QCD scattering Most often observed interaction at LHC Most often observed interaction at LHC Decay of heavy Standard Model (SM) particles Decay of heavy Standard Model (SM) particles Prominent example: Prominent example: Associated with particle production in Vector Boson Fusion (VBF) Associated with particle production in Vector Boson Fusion (VBF) E.g., Higgs E.g., Higgs Decay of Beyond Standard Model (BSM) particles Decay of Beyond Standard Model (BSM) particles E.g., SUSY E.g., SUSY electrons or muons jets missing transverse energy Where do Jets come from at LHC? 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c CERN-OPEN-2008- 020

9 P. Loch U of Arizona June 30, 2009 Final States with Missing E T Important signature for many standard model channels Important signature for many standard model channels Neutrino carry missing transverse energy Neutrino carry missing transverse energy W mass measurement in leptonic final state W mass measurement in leptonic final state Z decays to tau pairs Z decays to tau pairs Higgs decays to tau or W pairs Higgs decays to tau or W pairs New physics signatures New physics signatures Final states with neutrinos Final states with neutrinos MSSM Higgs (A) decays into tau pairs MSSM Higgs (A) decays into tau pairs Associated with W or invisible Z decays Associated with W or invisible Z decays New non-interacting particles New non-interacting particles Neutralinos, gluinos Neutralinos, gluinos Long lived particles Long lived particles Decay outside detector trigger window Decay outside detector trigger window 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c CERN-OPEN-2008- 020

10 P. Loch U of Arizona June 30, 2009 Final States with Missing E T Important signature for many standard model channels Important signature for many standard model channels Neutrino carry missing transverse energy Neutrino carry missing transverse energy W mass measurement in leptonic final state W mass measurement in leptonic final state Z decays to tau paris Z decays to tau paris Higgs decays to tau or W pairs Higgs decays to tau or W pairs New physics signatures New physics signatures Final states with neutrinos Final states with neutrinos MSSM Higgs (A) decays into tau pairs MSSM Higgs (A) decays into tau pairs Associated with W or invisible Z decays Associated with W or invisible Z decays New non-interacting particles New non-interacting particles Neutralinos, gluinos Neutralinos, gluinos Long lived particles Long lived particles Decay outside detector trigger window Decay outside detector trigger window 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c CERN-OPEN-2008- 020

11 P. Loch U of Arizona June 30, 2009 Collisions of other partons in the protons generating the signal interaction Collisions of other partons in the protons generating the signal interaction Unavoidable in hadron-hadron collisions Unavoidable in hadron-hadron collisions Independent soft to hard multi- parton interactions Independent soft to hard multi- parton interactions No real first principle No real first principle calculations calculations Contains low pT (non- pertubative) QCD Contains low pT (non- pertubative) QCD Tuning rather than calculations Tuning rather than calculations Activity shows some correlation with hard scattering (radiation) Activity shows some correlation with hard scattering (radiation) pTmin, pTmax differences pTmin, pTmax differences Typically tuned from data in physics generators Typically tuned from data in physics generators Carefully measured at Tevatron Carefully measured at Tevatron Phase space factor applied to LHC tune in absence of data Phase space factor applied to LHC tune in absence of data One of the first things to be measured at LHC One of the first things to be measured at LHC 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c Underlying Event

12 P. Loch U of Arizona June 30, 2009 Rick Field’s (CDF) view on di- jet events Collisions of other partons in the protons generating the signal interaction Collisions of other partons in the protons generating the signal interaction Unavoidable in hadron-hadron collisions Unavoidable in hadron-hadron collisions Independent soft to hard multi- parton interactions Independent soft to hard multi- parton interactions No real first principle No real first principle calculations calculations Contains low pT (non- pertubative) QCD Contains low pT (non- pertubative) QCD Tuning rather than calculations Tuning rather than calculations Activity shows some correlation with hard scattering (radiation) Activity shows some correlation with hard scattering (radiation) pTmin, pTmax differences pTmin, pTmax differences Typically tuned from data in physics generators Typically tuned from data in physics generators Carefully measured at Tevatron Carefully measured at Tevatron Phase space factor applied to LHC tune in absence of data Phase space factor applied to LHC tune in absence of data One of the first things to be measured at LHC One of the first things to be measured at LHC Look at activity (pT, # charged tracks) as function of leading jet pT in transverse region 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c Underlying Event

13 P. Loch U of Arizona June 30, 2009 CDF data (√s=1.8 TeV) LHC prediction: x2.5 the activity measured at Tevatron! pT leading jet (GeV) Number charged tracks in transverse region CDF data: Phys.Rev, D, 65 (2002) Collisions of other partons in the protons generating the signal interaction Collisions of other partons in the protons generating the signal interaction Unavoidable in hadron-hadron collisions Unavoidable in hadron-hadron collisions Independent soft to hard multi- parton interactions Independent soft to hard multi- parton interactions No real first principle No real first principle calculations calculations Contains low pT (non- pertubative) QCD Contains low pT (non- pertubative) QCD Tuning rather than calculations Tuning rather than calculations Activity shows some correlation with hard scattering (radiation) Activity shows some correlation with hard scattering (radiation) pTmin, pTmax differences pTmin, pTmax differences Typically tuned from data in physics generators Typically tuned from data in physics generators Carefully measured at Tevatron Carefully measured at Tevatron Phase space factor applied to LHC tune in absence of data Phase space factor applied to LHC tune in absence of data One of the first things to be measured at LHC One of the first things to be measured at LHC 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c Underlying Event

14 P. Loch U of Arizona June 30, 2009 Multiple interactions between partons in other protons in the same bunch crossing Multiple interactions between partons in other protons in the same bunch crossing Consequence of high rate (luminosity) and high proton-proton total cross- section (~75 mb) Consequence of high rate (luminosity) and high proton-proton total cross- section (~75 mb) Statistically independent of hard scattering Statistically independent of hard scattering Similar models used for soft physics as in underlying event Similar models used for soft physics as in underlying event Signal history in calorimeter increases noise Signal history in calorimeter increases noise Signal 10-20 times slower (ATLAS) than bunch crossing rate (25 ns) Signal 10-20 times slower (ATLAS) than bunch crossing rate (25 ns) Noise has coherent character Noise has coherent character Cell signals linked through past shower developments Cell signals linked through past shower developments E t ~ 58 GeV E t ~ 81 GeV without pile-up 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c Pile-Up Prog.Part.Nucl.Phys. 60:484-551,2008

15 P. Loch U of Arizona June 30, 2009 Multiple interactions between partons in other protons in the same bunch crossing Multiple interactions between partons in other protons in the same bunch crossing Consequence of high rate (luminosity) and high proton-proton total cross- section (~75 mb) Consequence of high rate (luminosity) and high proton-proton total cross- section (~75 mb) Statistically independent of hard scattering Statistically independent of hard scattering Similar models used for soft physics as in underlying event Similar models used for soft physics as in underlying event Signal history in calorimeter increases noise Signal history in calorimeter increases noise Signal 10-20 times slower (ATLAS) than bunch crossing rate (25 ns) Signal 10-20 times slower (ATLAS) than bunch crossing rate (25 ns) Noise has coherent character Noise has coherent character Cell signals linked through past shower developments Cell signals linked through past shower developments E t ~ 58 GeV E t ~ 81 GeV with design luminosity pile-up 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c Pile-Up Prog.Part.Nucl.Phys. 60:484-551,2008

16 P. Loch U of Arizona June 30, 2009 Multiple interactions between partons in other protons in the same bunch crossing Multiple interactions between partons in other protons in the same bunch crossing Consequence of high rate (luminosity) and high proton-proton total cross- section (~75 mb) Consequence of high rate (luminosity) and high proton-proton total cross- section (~75 mb) Statistically independent of hard scattering Statistically independent of hard scattering Similar models used for soft physics as in underlying event Similar models used for soft physics as in underlying event Signal history in calorimeter increases noise Signal history in calorimeter increases noise Signal 10-20 times slower (ATLAS) than bunch crossing rate (25 ns) Signal 10-20 times slower (ATLAS) than bunch crossing rate (25 ns) Noise has coherent character Noise has coherent character Cell signals linked through past shower developments Cell signals linked through past shower developments 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c Pile-Up Prog.Part.Nucl.Phys. 60:484-551,2008

17 P. Loch U of Arizona June 30, 2009 Why Is That Important? Jet calibration requirements very stringent Jet calibration requirements very stringent Systematic jet energy scale Systematic jet energy scale uncertainties to be extremely uncertainties to be extremely well controlled well controlled Top mass reconstruction Top mass reconstruction Relative jet energy resolution Relative jet energy resolution requirement requirement Inclusive jet cross-section Inclusive jet cross-section Di-quark mass spectra cut-off in SUSY Di-quark mass spectra cut-off in SUSY Event topology plays a role at 1% level of precision Event topology plays a role at 1% level of precision Extra particle production due to event color flow Extra particle production due to event color flow Color singlet (e.g., W) vs color octet (e.g., gluon/quark) jet source Color singlet (e.g., W) vs color octet (e.g., gluon/quark) jet source Small and large angle gluon radiation Small and large angle gluon radiation Quark/gluon jet differences Quark/gluon jet differences 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

18 P. Loch U of Arizona June 30, 2009 2. Jet Reconstruction in General 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

19 P. Loch U of Arizona June 30, 2009 Jet Algorithm Guidelines (1) Very important at LHC Very important at LHC Often LO (or even NLO) not sufficient to understand final states Often LO (or even NLO) not sufficient to understand final states Potentially significant K-factors can only be applied to jet driven spectra if jet finding follows theoretical rules Potentially significant K-factors can only be applied to jet driven spectra if jet finding follows theoretical rules E.g., jet cross-section shapes Need to be able to compare Need to be able to compare experiments and theory experiments and theory Comparison at the level of distributions Comparison at the level of distributions ATLAS and CMS will unfold experimental effects and limitations independently – different detector systems Theoretical guidelines Theoretical guidelines Infrared safety Infrared safety Adding or removing soft particles should not change the result of jet clustering Adding or removing soft particles should not change the result of jet clustering Collinear safety Collinear safety Splitting of large pT particle into two collinear particles should not affect the jet finding Splitting of large pT particle into two collinear particles should not affect the jet finding infrared sensitivity (soft gluon radiation merges jets) collinear sensitivity (2) (signal split into two towers below threshold) collinear sensitivity (1) (sensitive to E t ordering of seeds) 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

20 P. Loch U of Arizona June 30, 2009 Jet Algorithm Guidelines Detector technology independence Detector technology independence Jet efficiency should not depend on detector technology Jet efficiency should not depend on detector technology Final jet calibration and corrections ideally unfolds all detector effects Final jet calibration and corrections ideally unfolds all detector effects Minimal contribution from spatial and energy resolution to reconstructed jet kinematics Minimal contribution from spatial and energy resolution to reconstructed jet kinematics Unavoidable intrinsic detector limitations set limits Unavoidable intrinsic detector limitations set limits Stability within environment Stability within environment (Electronic) detector noise should not affect jet reconstruction within reasonable limits (Electronic) detector noise should not affect jet reconstruction within reasonable limits Energy resolution limitation Energy resolution limitation Avoid energy scale shift due to noise Avoid energy scale shift due to noise Stability with changing (instantaneous) luminosity at LHC Stability with changing (instantaneous) luminosity at LHC Control of underlying event and pile-up signal contribution Control of underlying event and pile-up signal contribution “Easy” to calibrate “Easy” to calibrate Small algorithm bias for jet signal Small algorithm bias for jet signal Probably means high signal stability Probably means high signal stability High reconstruction efficiency High reconstruction efficiency Identify all physically interesting jets from energetic partons in perturbative QCD Identify all physically interesting jets from energetic partons in perturbative QCD Jet reconstruction in resonance decays Jet reconstruction in resonance decays High efficiency to separate close-by jets from same particle decay High efficiency to separate close-by jets from same particle decay Least sensitivity to boost of particle Least sensitivity to boost of particle 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

21 P. Loch U of Arizona June 30, 2009 Iterative Cone Algorithms Seeded fixed cones Seeded fixed cones Geometrically motivated jet finders Geometrically motivated jet finders Collect particles or detector signals into fixed sized cone of chosen radius R Collect particles or detector signals into fixed sized cone of chosen radius R Basic parameters are seed pT threshold and cone size Basic parameters are seed pT threshold and cone size Theoretical concerns Theoretical concerns Seed introduces collinear instability Seed introduces collinear instability One particle above seed splits into two collinear particles below seed Infrared safety Infrared safety Cone formation sensitive to low energetic particles Can ignore large signal due to change of direction at each iteration (e.g., “dark towers” @ CDF) Addressed by split and merge Addressed by split and merge One more parameter – see later Seedless fixed cones Seedless fixed cones No seeds No seeds Collect particles around any other particle into a fixed cone of chosen radius Collect particles around any other particle into a fixed cone of chosen radius Otherwise similar to seeded cone Otherwise similar to seeded cone Theoretical issues Theoretical issues Collinear stability – no issue Collinear stability – no issue Infrared safety Infrared safety Higher stabilty but still needs split and merge Often considered “simple” Often considered “simple” Implementation obvious Implementation obvious Especially seeded cone Especially seeded cone Computational limitations on seedless cone Computational limitations on seedless cone LHC O(1000) particles possible Provide an area Provide an area Careful – split and merge makes shape less regular Careful – split and merge makes shape less regular 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

22 P. Loch U of Arizona June 30, 2009 Recursive Recombination Attempt to undo parton fragmentation Attempt to undo parton fragmentation QCD branching happens all the time QCD branching happens all the time Pair with strongest divergency between them likely belongs together Pair with strongest divergency between them likely belongs together kT/Durham first used in e + e - kT/Durham first used in e + e - Longitudinal invariant version for hadron colliders Longitudinal invariant version for hadron colliders Catani, Dokshitzer, Seymour & Webber ’93 Catani, Dokshitzer, Seymour & Webber ’93 S.D. Ellis & D. Soper ’93 S.D. Ellis & D. Soper ’93 Modern implementations Modern implementations kT with clustering sequence using pT and distance parameter kT with clustering sequence using pT and distance parameter Ordered in kT, sequence follows jet structure Durham/Aachen clustering sequence using angular distance Durham/Aachen clustering sequence using angular distance Ordered in angular distance, sequence follows jet structure Anti-kT using pT and distance parameter with inverted sequence Anti-kT using pT and distance parameter with inverted sequence No particular ordering, sequence not meaning full Valid at all orders! Valid at all orders! 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c arXiv:0802.1189v1 [hep-ph] 8 Feb 2008

23 P. Loch U of Arizona June 30, 2009 Recursive Recombination Algorithms 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

25 P. Loch U of Arizona June 30, 2009 Recursive Recombination Algortihms 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

26 P. Loch U of Arizona June 30, 2009 Recursive Recombination Algortihms 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

33 P. Loch U of Arizona June 30, 2009 Seeded Cone Algorithm Algorithm flow Algorithm flow Find seed with Et > Etmin Find seed with Et > Etmin Collect all particles within R of seed Collect all particles within R of seed Typical R = 0.4, R = 0.7 Typical R = 0.4, R = 0.7 Recalculate new cone direction Recalculate new cone direction Collect particles in new cone and find new direction Collect particles in new cone and find new direction Stop if cone is stable Stop if cone is stable There may not be a stable solution! There may not be a stable solution! Interative cone with removal (CMS) Interative cone with removal (CMS) Remove particles assigned to the cone from list and find next seed Remove particles assigned to the cone from list and find next seed Find next surviving seed and new cone Find next surviving seed and new cone Stop if no more seeds or stable cones Stop if no more seeds or stable cones Overlapping cones (ATLAS) Overlapping cones (ATLAS) Find next seed and new cone until all seeds are exhausted Find next seed and new cone until all seeds are exhausted Apply split and merge procedure Apply split and merge procedure Split and merge Split and merge Merge jets Merge jets Use pT of overlapping constituents above fraction f of pT of lower pT jet Use pT of overlapping constituents above fraction f of pT of lower pT jet f = 50% in ATLAS Otherwise split lower pT jet Otherwise split lower pT jet Assign split constituents to higher pT jet Assign split constituents to higher pT jet Theoretical concerns Theoretical concerns Collinear safety Collinear safety “Tower signal” can split seeds “Tower signal” can split seeds Infrared safety Infrared safety Additional soft particles affect cone formation and stabilitiy Additional soft particles affect cone formation and stabilitiy Stable solution may leave significant energy unclustered Stable solution may leave significant energy unclustered Addressed by variations Addressed by variations E.g., Mid-point cone at Tevatron places seeds between particles E.g., Mid-point cone at Tevatron places seeds between particles 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

34 P. Loch U of Arizona June 30, 2009 Limitations of Cone Algorithms Limitations for physics final state calculations Limitations for physics final state calculations G. Salam, 2008 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

35 P. Loch U of Arizona June 30, 2009 Seedless Cone Algorithm Infrared safe seedless cone Infrared safe seedless cone SISCone (Salam, Soyez 2007) SISCone (Salam, Soyez 2007) No collinear issue in the absence of seed thresholds No collinear issue in the absence of seed thresholds Available for use in ATLAS & CMS Available for use in ATLAS & CMS Finds all stable cones in all particles Finds all stable cones in all particles Avoid infrared safety issues Avoid infrared safety issues Apply split and merge to those Apply split and merge to those Recommended high split & merge fraction f = 75% suppresses merging Recommended high split & merge fraction f = 75% suppresses merging Uses geometry principles for faster execution Uses geometry principles for faster execution Time ~N 2 logN not as fast as latest kT but at least manageable Time ~N 2 logN not as fast as latest kT but at least manageable 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

36 P. Loch U of Arizona June 30, 2009 We need to be able to talk to each other We need to be able to talk to each other CMS ↔ ATLAS, Tevatron ↔ LHC experiments, experiments ↔ theory CMS ↔ ATLAS, Tevatron ↔ LHC experiments, experiments ↔ theory Need to agree on a jet definition Need to agree on a jet definition Simple proposal out since Les Houches 2007 Simple proposal out since Les Houches 2007 Specify algorithm, all parameters, signal choice, cuts, and recombination strategy Specify algorithm, all parameters, signal choice, cuts, and recombination strategy Obvious but rarely done at required level of completeness and precision Obvious but rarely done at required level of completeness and precision Requires common software implementations Requires common software implementations Cannot allow misunderstandings in private implementations – no way to compare!! Cannot allow misunderstandings in private implementations – no way to compare!! ATLAS & CMS use same code for most algorithms ATLAS & CMS use same code for most algorithms FastJet libraries (Salam et al.) FastJet libraries (Salam et al.) Provides kT flavours, SISCone and legacy cones Provides kT flavours, SISCone and legacy cones Includes CMS and ATLAS specific implementations of cone algorithms Includes CMS and ATLAS specific implementations of cone algorithms 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c “Snowmass” 4-momentum Jet Definition

37 P. Loch U of Arizona June 30, 2009 Interesting recent papers on jet issues at LHC Interesting recent papers on jet issues at LHC Towards Jetography Towards Jetography G.P. Salam, arXiv-0906.1833v1 [hep-ph] 10 June 2009 G.P. Salam, arXiv-0906.1833v1 [hep-ph] 10 June 2009 Quantifying the Performance of Jet Definitions for Kinematic Reconstruction at LHC Quantifying the Performance of Jet Definitions for Kinematic Reconstruction at LHC M. Cacciari, J. Rojo, G.P. Salam, G. Soyez, JHEP 0812:032,2008 M. Cacciari, J. Rojo, G.P. Salam, G. Soyez, JHEP 0812:032,2008 Standard Model Handles and Candles Working Group: Tools and Jets Summary Report Standard Model Handles and Candles Working Group: Tools and Jets Summary Report C. Buttar et al., arXiv:0803.0678 [hep-ph] March 2008 C. Buttar et al., arXiv:0803.0678 [hep-ph] March 2008 The Anti-k(t) Jet Clustering Algorithm The Anti-k(t) Jet Clustering Algorithm M. Cacciari, G.P. Salam, G. Soyez, JHEP 0804:063,2008 M. Cacciari, G.P. Salam, G. Soyez, JHEP 0804:063,2008 (original idea by P.A. Delsart, ATLAS) (original idea by P.A. Delsart, ATLAS) Non-perturbative QCD effects in jets at hadron colliders Non-perturbative QCD effects in jets at hadron colliders M. Dasgupta, L. Magnea, G.P. Salam, JHEP 0802:055,2008 M. Dasgupta, L. Magnea, G.P. Salam, JHEP 0802:055,2008 Pileup subtraction using jet areas Pileup subtraction using jet areas M. Cacciari, G.P. Salam, Phys.Lett.B659:119-126,2008 M. Cacciari, G.P. Salam, Phys.Lett.B659:119-126,2008 A Practical Seedless Infrared-Safe Cone jet algorithm A Practical Seedless Infrared-Safe Cone jet algorithm G.P. Salam, G. Soyez, JHEP 0705:086,2007. G.P. Salam, G. Soyez, JHEP 0705:086,2007. 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c Bibliography

38 P. Loch U of Arizona June 30, 2009 Experimental Jetology 101 What are jets for What are jets for experimentalists? experimentalists? A bunch of particles generated A bunch of particles generated by hadronization of a common by hadronization of a common source source Quark, gluon fragmenation Quark, gluon fragmenation As a consequence, the particles As a consequence, the particles in this bunch have correlated in this bunch have correlated kinematic properties kinematic properties Reflecting the source by sum Reflecting the source by sum rules/conservations rules/conservations The interacting particles in this The interacting particles in this bunch generated an observable bunch generated an observable signal in a detector signal in a detector Protons, neutrons, pions, photons, Protons, neutrons, pions, photons, electrons, muons, other particles with laboratory lifetimes >~10ps, and the corresponding anti-particles electrons, muons, other particles with laboratory lifetimes >~10ps, and the corresponding anti-particles The non-interacting particles do not generate a directly observable signal The non-interacting particles do not generate a directly observable signal Neutrinos, mostly Neutrinos, mostly What is jet reconstruction, then? What is jet reconstruction, then? Model/simulation: particle jet Model/simulation: particle jet Attempt to collect the final state particles described above into objects (jets) representing the original parton kinematic Attempt to collect the final state particles described above into objects (jets) representing the original parton kinematic Re-establishing the correlations Re-establishing the correlations Experiment: detector jet Experiment: detector jet Attempt to collect the detector signals from these particles to measure their original kinematics Attempt to collect the detector signals from these particles to measure their original kinematics Usually not the parton! Usually not the parton! 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c CERN-OPEN-2008- 020

39 P. Loch U of Arizona June 30, 2009 Change of composition Change of composition Radiation and decay inside detector volume Radiation and decay inside detector volume “Randomization” of original particle content “Randomization” of original particle content Defocusing changes shape in lab frame Defocusing changes shape in lab frame Charged particles bend in solenoid field Charged particles bend in solenoid field Attenuation changes energy Attenuation changes energy Total loss of soft charged particles in magnetic field Total loss of soft charged particles in magnetic field Partial and total energy loss of charged and neutral particles in inactive upstream material Partial and total energy loss of charged and neutral particles in inactive upstream material Hadronic and electromagnetic cacades in calorimeters Hadronic and electromagnetic cacades in calorimeters Distribute energy spatially Distribute energy spatially Lateral particle shower overlap Lateral particle shower overlap 100 MeV 10 GeV 1 GeV Image of Jet in the Detector 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

42 P. Loch U of Arizona June 30, 2009 Change of composition Change of composition Radiation and decay inside detector volume Radiation and decay inside detector volume “Randomization” of original particle content “Randomization” of original particle content Defocusing changes shape in lab frame Defocusing changes shape in lab frame Charged particles bend in solenoid field Charged particles bend in solenoid field Attenuation changes energy Attenuation changes energy Total loss of soft charged particles in magnetic field Total loss of soft charged particles in magnetic field Partial and total energy loss of charged and neutral particles in inactive upstream material Partial and total energy loss of charged and neutral particles in inactive upstream material Hadronic and electromagnetic cacades in calorimeters Hadronic and electromagnetic cacades in calorimeters Distribute energy spatially Distribute energy spatially Lateral particle shower overlap Lateral particle shower overlap 100 MeV 10 GeV 1 GeV 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c Image of Jet in the Detector

43 P. Loch U of Arizona June 30, 2009 Change of composition Change of composition Radiation and decay inside detector volume Radiation and decay inside detector volume “Randomization” of original particle content “Randomization” of original particle content Defocusing changes shape in lab frame Defocusing changes shape in lab frame Charged particles bend in solenoid field Charged particles bend in solenoid field Attenuation changes energy Attenuation changes energy Total loss of soft charged particles in magnetic field Total loss of soft charged particles in magnetic field Partial and total energy loss of charged and neutral particles in inactive upstream material Partial and total energy loss of charged and neutral particles in inactive upstream material Hadronic and electromagnetic cacades in calorimeters Hadronic and electromagnetic cacades in calorimeters Distribute energy spatially Distribute energy spatially Lateral particle shower overlap Lateral particle shower overlap 100 MeV 10 GeV 1 GeV 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c Image of Jet in the Detector

44 P. Loch U of Arizona June 30, 2009 Experiment (“Nature”) Jet Reconstruction Tasks

45 P. Loch U of Arizona June 30, 2009 Experiment (“Nature”) Particles UE MB Multiple Interactions Stable Particles Decays Jet Finding Particle Jets Generated Particles Generated Particles Modeling Particle Jets Jet Reconstruction Tasks

46 P. Loch U of Arizona June 30, 2009 Jet Reconstruction Task Overview Modeling Calorimeter Jets Experiment (“Nature”) Reconstructed Jets Stable Particles Raw Calorimeter Signals Detector Simulation Reconstructed Calorimeter Signals Signal Reconstruction Jet Finding Identified Particles

47 P. Loch U of Arizona June 30, 2009 Jet Reconstruction Task Overview Measuring Calorimeter Jets Experiment (“Nature”) Reconstructed Jets Observable Particles Raw Calorimeter Signals Measurement Reconstructed Calorimeter Signals Signal Reconstruction Jet Finding Identified Particles

48 P. Loch U of Arizona June 30, 2009 Jet Reconstruction Task Overview Jet Reconstruction Challenges physics reaction of interest (interaction or parton level) added tracks from underlying event added tracks from in-time (same trigger) pile-up event jet reconstruction algorithm efficiency added tracks from underlying event added tracks from in-time (same trigger) pile-up event jet reconstruction algorithm efficiency longitudinal energy leakage detector signal inefficiencies (dead channels, HV…) pile-up noise from (off- and in-time) bunch crossings electronic noise calo signal definition (clustering, noise suppression…) dead material losses (front, cracks, transitions…) detector response characteristics (e/h ≠ 1) jet reconstruction algorithm efficiency lost soft tracks due to magnetic field longitudinal energy leakage detector signal inefficiencies (dead channels, HV…) pile-up noise from (off- and in-time) bunch crossings electronic noise calo signal definition (clustering, noise suppression…) dead material losses (front, cracks, transitions…) detector response characteristics (e/h ≠ 1) jet reconstruction algorithm efficiency lost soft tracks due to magnetic field Experiment (“Nature”)

49 P. Loch U of Arizona June 30, 2009 3. Jet Measurements 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

50 P. Loch U of Arizona June 30, 2009 Total weight : 7000 t Overall length: 46 m Overall diameter: 23 m Magnetic field: 2T solenoid + toroid 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c The ATLAS Detector

51 P. Loch U of Arizona June 30, 2009 The CMS Detector Total weight: 12500 t Overall length: 22 m Overall diameter: 15 m Magnetic field: 4T solenoid 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

52 P. Loch U of Arizona June 30, 2009 Calorimeters Side-by-Side from “Jets & Heavy Flavour at LHC”, A.-M. Magnan, Photon09, May 2009 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

53 P. Loch U of Arizona June 30, 2009 More complex than EM showers More complex than EM showers Visible EM O(50%) Visible EM O(50%) e , ,  o  e , ,  o  Visible non-EM O(25%) Visible non-EM O(25%) Ionization of  , p,   Ionization of  , p,   Invisible O(25%) Invisible O(25%) Nuclear break-up Nuclear break-up Nuclear excitation Nuclear excitation Escaped O(2%) Escaped O(2%) Neutrinos produced in shower Neutrinos produced in shower Only part of the visible energy is sampled into the signal Only part of the visible energy is sampled into the signal No intrinsic compensation for losses in ATLAS or CMS No intrinsic compensation for losses in ATLAS or CMS Both are non-compensating calorimeters Both are non-compensating calorimeters Electron signal on average larger than pion signal at same deposited energy (e/h > 1) Electron signal on average larger than pion signal at same deposited energy (e/h > 1) Introduces complex jet response Introduces complex jet response Mixture of electromgnetic and hadronic signals in a priori unknown fluctuations Mixture of electromgnetic and hadronic signals in a priori unknown fluctuations EM shower RD3 note 41, 28 Jan 1993 Grupen, Particle Detectors Quick Look at Hadronic Showers 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

54 P. Loch U of Arizona June 30, 2009 Electromagnetic Electromagnetic Compact Compact Growths in depth ~log(E) Growths in depth ~log(E) Longitudinal extension scale is radiation length X 0 Longitudinal extension scale is radiation length X 0 Distance in matter in which ~50% of electron energy is radiated off Distance in matter in which ~50% of electron energy is radiated off Photons 9/7 X 0 Photons 9/7 X 0 Strong correlation between lateral and longitudinal shower development Strong correlation between lateral and longitudinal shower development Small intrinsic shower-to- shower fluctuations Small intrinsic shower-to- shower fluctuations Very regular development Very regular development Can be simulated with high precision Can be simulated with high precision 1% or better, depending on features 1% or better, depending on features Hadronic Hadronic Scattered, significantly bigger Scattered, significantly bigger Growths in depth ~log(E) Growths in depth ~log(E) Longitudinal extension scale is interaction length λ Longitudinal extension scale is interaction length λ Average distance between two inelastic interactions in matter Average distance between two inelastic interactions in matter Varies significantly for pions, protons, neutrons Varies significantly for pions, protons, neutrons Weak correlation between longitudinal and lateral shower development Weak correlation between longitudinal and lateral shower development Large intrinsic shower-to- shower fluctuations Large intrinsic shower-to- shower fluctuations Very irregular development Very irregular development Can be simulated with reasonable precision Can be simulated with reasonable precision ~2-5% depending on feature ~2-5% depending on feature Showers Side-by-Side 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

55 P. Loch U of Arizona June 30, 2009 Signals from Hadronic Showers Signal components Signal components Hadronic signal contributions change with energy Hadronic signal contributions change with energy Visible non-EM fraction decreases with E Visible non-EM fraction decreases with E Shower look more electromagnetic at high energies Shower look more electromagnetic at high energies Hadron response not linear with E Hadron response not linear with E Invisible energy is the main source of e/h > 1 Invisible energy is the main source of e/h > 1 Different signals from intrinsic em showers and ionizations by charged hadrons Different signals from intrinsic em showers and ionizations by charged hadrons Large fluctuations of each component fraction lead to large signal fluctuations Large fluctuations of each component fraction lead to large signal fluctuations Intrinsic fluctuations bigger than sampling fluctuations Intrinsic fluctuations bigger than sampling fluctuations Electromagnetic energy scale calibration does not recover loss Electromagnetic energy scale calibration does not recover loss Basic signal scale in calorimeters is defined by electron response Basic signal scale in calorimeters is defined by electron response Linear response to whole shower Linear response to whole shower All processes leave observable signal – in principle All processes leave observable signal – in principle Need additional corrections to measure hadron or jet energy Need additional corrections to measure hadron or jet energy Typically non-linear response corrections to restore scale Typically non-linear response corrections to restore scale Additional attempt to reduce fluctuations Additional attempt to reduce fluctuations Increased complexity due to Increased complexity due to 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c T.A. Gabriel, D.E. Groom, Nucl. Instr. Meth. A338 (1994) 336 Energy (GeV) Response Ratio

56 P. Loch U of Arizona June 30, 2009 Calorimeter Cells Smallest signal collection volume Smallest signal collection volume Defines resolution of spatial structures Defines resolution of spatial structures Finest granularity depends on direction and sampling layer in ATLAS Finest granularity depends on direction and sampling layer in ATLAS Each is read out independently Each is read out independently Can generate more than one signal (e.g., ATLAS Tile cells feature two readouts) Can generate more than one signal (e.g., ATLAS Tile cells feature two readouts) Individual cell signals Individual cell signals Sensitive to noise (different for CMS and ATLAS) Sensitive to noise (different for CMS and ATLAS) Fluctuations in electronics gain and shaping Fluctuations in electronics gain and shaping Time jitters Time jitters Physics sources like multiple proton interaction history in pile-up Physics sources like multiple proton interaction history in pile-up Hard to calibrate for hadrons Hard to calibrate for hadrons No measure to determine if electromagnetic or hadronic in cell signal alone, i.e. no handle to estimate e/h No measure to determine if electromagnetic or hadronic in cell signal alone, i.e. no handle to estimate e/h Need signal neighbourhood and/or other detectors to calibrate Need signal neighbourhood and/or other detectors to calibrate Basic energy scale Basic energy scale Use electron calibration to establish basic energy scale for cell signals Use electron calibration to establish basic energy scale for cell signals Cell geometry Cell geometry Quasi-projective by pointing to the nominal collision vertex in ATLAS and CMS Quasi-projective by pointing to the nominal collision vertex in ATLAS and CMS Lateral sizes scale with pseudo-rapidity and azimuthal angular opening Lateral sizes scale with pseudo-rapidity and azimuthal angular opening Non-projective in ATLAS Forward Calorimeter Non-projective in ATLAS Forward Calorimeter 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

57 P. Loch U of Arizona June 30, 2009 Impose a regular grid view on event Impose a regular grid view on event Δ  ×Δφ = 0.1×0.1 grid Δ  ×Δφ = 0.1×0.1 grid Motivated by particle Et flow in hadron-hadron collisions Motivated by particle Et flow in hadron-hadron collisions Well suited for trigger purposes Well suited for trigger purposes Collect cells into tower grid Collect cells into tower grid Cells EM scale signals are summed with geometrical weights Cells EM scale signals are summed with geometrical weights Depend on cell area containment ratio Depend on cell area containment ratio Weight = 1 for projective cells of equal or smaller than tower size Weight = 1 for projective cells of equal or smaller than tower size Summing can be selective Summing can be selective See jet input signal discussion See jet input signal discussion Towers have massless four- momentum representation Towers have massless four- momentum representation Fixed direction given by geometrical grid center Fixed direction given by geometrical grid center projective cells non-projectivecells ATLAS Calorimeter Towers 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

58 P. Loch U of Arizona June 30, 2009 Signal extraction tool Signal extraction tool Attempt reconstruction of Attempt reconstruction of individual particle showers individual particle showers Reconstruct 3-dim clusters of Reconstruct 3-dim clusters of cells with correlated signals cells with correlated signals Use shape of these clusters to Use shape of these clusters to locally calibrate them locally calibrate them Explore differences between Explore differences between electromagnetic and hadronic electromagnetic and hadronic shower development and select shower development and select best suited calibration best suited calibration Supress noise with least bias on physics signals Supress noise with least bias on physics signals Often less than 50% of all cells in an event with “real” signal Often less than 50% of all cells in an event with “real” signal Some implications of jet environment Some implications of jet environment Shower overlap cannot always be resolved Shower overlap cannot always be resolved Clusters represent merged particle showers in dense jets Clusters represent merged particle showers in dense jets Clusters have varying sizes Clusters have varying sizes No simple jet area as in case of towers No simple jet area as in case of towers Clusters are mass-less 4-vectors (as towers) Clusters are mass-less 4-vectors (as towers) No “artificial” mass contribution due to showering No “artificial” mass contribution due to showering Issues with IR safety at very small scale insignificant Issues with IR safety at very small scale insignificant Pile-Up environment triggers split as well as merge Pile-Up environment triggers split as well as merge Note that calorimeters themselves are not completely IR safe Note that calorimeters themselves are not completely IR safe 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c ATLAS Topological Cell Clusters (1) JINST 3:S08003, 2008

59 P. Loch U of Arizona June 30, 2009 Cluster seeding Cluster seeding Cluster seed is cell with significant signal above a primary threshold Cluster seed is cell with significant signal above a primary threshold Cluster growth: direct neighbours Cluster growth: direct neighbours Neighbouring cells (in 3-d) with cell signal significance above some basic threshold are collected Neighbouring cells (in 3-d) with cell signal significance above some basic threshold are collected Cluster growth: control of expansion Cluster growth: control of expansion Collect neighbours of neighbours for cells above secondary signal significance threshold Collect neighbours of neighbours for cells above secondary signal significance threshold Secondary threshold lower than primary (seed) threshold Secondary threshold lower than primary (seed) threshold Cluster splitting Cluster splitting Analyze clusters for local signal maxima and split if more than one found Analyze clusters for local signal maxima and split if more than one found Signal hill & valley analysis in 3-d Signal hill & valley analysis in 3-d Final “energy blob” can contain low signal cells Final “energy blob” can contain low signal cells Cells survive due to significant neighbouring signal Cells survive due to significant neighbouring signal Cells inside blob can have negative signals Cells inside blob can have negative signals ATLAS also studies “TopoTowers” ATLAS also studies “TopoTowers” Use topological clustering as noise suppression tool only Use topological clustering as noise suppression tool only Distribute only energy of clustered cells onto tower grid Distribute only energy of clustered cells onto tower grid Motivated by DZero approach Motivated by DZero approach ATLAS Topological Cell Clusters (2) 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

60 P. Loch U of Arizona June 30, 2009 cluster candidate #1 cluster candidate #2 #3? ATLAS Topological Cell Clusters (3) 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

61 P. Loch U of Arizona June 30, 2009 ATLAS Local Hadronic Calibration Local hadronic energy scale restauraion depends on origin of calorimeter signal Local hadronic energy scale restauraion depends on origin of calorimeter signal Attempt to classify energy deposit as electromagnetic or hadronic from the cluster signal and shape Attempt to classify energy deposit as electromagnetic or hadronic from the cluster signal and shape Allows to apply specific corrections and calibrations Allows to apply specific corrections and calibrations Local calibration approach Local calibration approach Use topological cell clusters as signal base for a hadronic energy scale Use topological cell clusters as signal base for a hadronic energy scale Recall cell signals need context for hadronic calibration Recall cell signals need context for hadronic calibration Basic concept is to reconstruct the locally deposited energy from the cluster signal first Basic concept is to reconstruct the locally deposited energy from the cluster signal first This is not the particle energy This is not the particle energy Additional corrections for energy losses with some correlation to the cluster signals and shapes extend the local scope Additional corrections for energy losses with some correlation to the cluster signals and shapes extend the local scope True signal loss due to the noise suppression in the cluster algorithm (still local) True signal loss due to the noise suppression in the cluster algorithm (still local) Dead material losses in front of, or between sensitive calorimeter volumes (larger scope than local deposit) Dead material losses in front of, or between sensitive calorimeter volumes (larger scope than local deposit) After all corrections, the reconstructed energy is on average the isolated particle energy After all corrections, the reconstructed energy is on average the isolated particle energy E.g., in a testbeam E.g., in a testbeam But not the jet energy (see later) But not the jet energy (see later) 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

62 P. Loch U of Arizona June 30, 2009 ATLAS Local Scale Sequence Electronic and readout effects unfolded (nA->GeV calibration) 3-d topological cell clustering includes noise suppression and establishes basic calorimeter signal for further processing Cluster shape analysis provides appropriate classification for calibration and corrections Cluster character depending calibration (cell signal weighting for HAD, to be developed for EM?) Apply dead material corrections specific for hadronic and electromagnetic clusters, resp. Apply specific out-of-cluster corrections for hadronic and electromagnetic clusters, resp.

63 P. Loch U of Arizona June 30, 2009 CMS Energy Flow Signal Attempt to reconstruct each Attempt to reconstruct each particle separately particle separately Start with topological calorimeter Start with topological calorimeter signal clusters signal clusters Clusters formed in similar way as in Clusters formed in similar way as in ATLAS ATLAS Hadronic cluster calibration depends Hadronic cluster calibration depends on energy sharing between sections on energy sharing between sections Use energy Use energy flow flow approach approach Replace Replace cluster cluster signal signal with with better better measured measured tracks for charged hadrons and identified muons tracks for charged hadrons and identified muons CMS PAS PFT-09/001 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

64 P. Loch U of Arizona June 30, 2009 CMS Link Algorithm Links between detector signals Links between detector signals Based on pseudo-rapidity and azimuth difference between clusters and tracks Based on pseudo-rapidity and azimuth difference between clusters and tracks Between clusters and tracks Between clusters and tracks Between clusters in ECAL and in HCAL Between clusters in ECAL and in HCAL Establish all links Establish all links Direction overlap criteria apply Direction overlap criteria apply Particle-flow charged hadrons Particle-flow charged hadrons Cluster(s) linked to tracks have less signal than track pT suggests Cluster(s) linked to tracks have less signal than track pT suggests Charged hadron hypothesis: momentum and energy from track Charged hadron hypothesis: momentum and energy from track Pion mass assumption Cluster(s) and tracks have comparable signal Cluster(s) and tracks have comparable signal Total charged hadron energy from error-weighted mean of track and calorimeter signals Total charged hadron energy from error-weighted mean of track and calorimeter signals Particle flow photons and neutral hadrons Particle flow photons and neutral hadrons Cluster(s) have significant energy excess with respect to closests track Cluster(s) have significant energy excess with respect to closests track If excess larger than ECAL energy, photon is created from ECAL energy and neutral hadron is created from HCAL If excess larger than ECAL energy, photon is created from ECAL energy and neutral hadron is created from HCAL CMS PAS PFT-09/001 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

65 P. Loch U of Arizona June 30, 2009 Jet Input Signals (1) Electromagnetic energy scale signals in calorimeters Electromagnetic energy scale signals in calorimeters ATLAS basic towers ATLAS basic towers No noise suppression, all cells (~190,000) used No noise suppression, all cells (~190,000) used Noise cancellation by re-summation of negative tower signals with near-by positive signals Noise cancellation by re-summation of negative tower signals with near-by positive signals Et(tower) > 0 threshold Et(tower) > 0 threshold ATLAS topological clusters & towers ATLAS topological clusters & towers Noise suppressed by topological cell cluster formation Noise suppressed by topological cell cluster formation Different signal geometry Different signal geometry Et(tower) > 0 threshold Et(tower) > 0 threshold CMS towers CMS towers No cell noise suppression No cell noise suppression Et(tower) > 500 MeV(1 GeV?) threshold Et(tower) > 500 MeV(1 GeV?) threshold Particle or hadronic scale signals Particle or hadronic scale signals CMS particle flow CMS particle flow Fully reconstructed particle flow with charged and neutral hadrons, electrons and photons (isolated/non-isolated), photons, muons Fully reconstructed particle flow with charged and neutral hadrons, electrons and photons (isolated/non-isolated), photons, muons Follow event particle flow as much as possible Follow event particle flow as much as possible ATLAS local hadronic scale ATLAS local hadronic scale Locally calibrated topological cell clusters Locally calibrated topological cell clusters Correlated with event particle flow but no explicite particle reconstruction Correlated with event particle flow but no explicite particle reconstruction 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

66 P. Loch U of Arizona June 30, 2009 Jet Input Signals (2) ATLAS ATLAS Basic 0.1 x 0.1 towers Basic 0.1 x 0.1 towers All cells (~190,000) projected into towers All cells (~190,000) projected into towers Electromagnetic energy scale signal Electromagnetic energy scale signal No noise suppression but noise cancellation attempt No noise suppression but noise cancellation attempt Topological 0.1 x 0.1 towers Topological 0.1 x 0.1 towers Cells from topological clusters only Cells from topological clusters only Electromagnetic energy scale signal Electromagnetic energy scale signal Noise suppression like for topological clusters Noise suppression like for topological clusters Topological cell clusters Topological cell clusters Electromagnetic and local hadronic scale signals Electromagnetic and local hadronic scale signals Noise suppressed Noise suppressed Tower and clusters create different jets Tower and clusters create different jets Cluster sequences different Cluster sequences different Shapes different Shapes different CMS CMS Basic towers 0.1 x 0.1 Basic towers 0.1 x 0.1 Electromagnetic scale signal Electromagnetic scale signal Et > 1 GeV (500 MeV) noise suppression for each tower Et > 1 GeV (500 MeV) noise suppression for each tower Energy flow objects provide particle level signal representation Energy flow objects provide particle level signal representation Charged hadrons Charged hadrons Neutral hadrons Neutral hadrons Muons Muons Electrons (isolated and non- isolated) Electrons (isolated and non- isolated) Photons Photons Both: Both: Reconstructed tracks Reconstructed tracks Track jets Track jets Stable truth particles in simulations Stable truth particles in simulations Truth jets Truth jets Fast simulation pseudo-cells, - towers, -clusters Fast simulation pseudo-cells, - towers, -clusters 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

67 P. Loch U of Arizona June 30, 2009 Jet Input Signals (3) 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c from K. Perez, Columbia U.

68 P. Loch U of Arizona June 30, 2009 Jets in the ATLAS Calorimeters 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c S.D. Ellis, J. Huston, K. Hatakeyama, P. Loch, M. Toennesmann, Prog.Part.Nucl.Phys.60:484-551,2008

69 P. Loch U of Arizona June 30, 2009 Sequential process Sequential process Input signal selection Input signal selection Get the best signals out of your detector on a given signal scale Get the best signals out of your detector on a given signal scale Preparation for jet finding Preparation for jet finding Suppression/cancellation of “unphysical” signal objects with E<0 (due to noise) Suppression/cancellation of “unphysical” signal objects with E<0 (due to noise) Possibly event ambiguity resolution (remove reconstructed electrons, photons, taus,… from detector signal) Possibly event ambiguity resolution (remove reconstructed electrons, photons, taus,… from detector signal) Not done in ATLAS before jet reconstruction! Pre-clustering to speed up reconstruction (not needed anymore) Pre-clustering to speed up reconstruction (not needed anymore) Jet finding Jet finding Apply your jet finder of choice Apply your jet finder of choice Jet calibration Jet calibration Depending on detector input signal definition, jet finder choices, references… Depending on detector input signal definition, jet finder choices, references… Jet selection Jet selection Apply cuts on kinematics etc. to select jets of interest or significance Apply cuts on kinematics etc. to select jets of interest or significance Objective Objective Reconstruct particle level features Reconstruct particle level features Test models and extract physics Test models and extract physics “How-to” of Jet Reconstruction 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

70 P. Loch U of Arizona June 30, 2009 Monte Carlo Jet Calibration Typical Monte Carlo based normalization Typical Monte Carlo based normalization Match particle level jets with detector jets in simple topologies (fully simulated QCD di-jets) Match particle level jets with detector jets in simple topologies (fully simulated QCD di-jets) Use same specific jet definition for both Use same specific jet definition for both Match defined by maximum angular distance Match defined by maximum angular distance Can include isolation requirements Determine calibration function parameters using truth particle jet energy constraint Determine calibration function parameters using truth particle jet energy constraint Fit calibration parameters such that relative energy resolution is best Fit calibration parameters such that relative energy resolution is best Include whole phase space into fit (flat in energy) Correct residual non-linearities by jet energy scale correction function Correct residual non-linearities by jet energy scale correction function Numerical inversion technique applied here Numerical inversion technique applied here Little factorization here Little factorization here Magnitudes of calibrations and corrections depend on signal choices Magnitudes of calibrations and corrections depend on signal choices Electromagnetic energy scale signals require large corrections while particle level or local hadronic signal have much less corrections Electromagnetic energy scale signals require large corrections while particle level or local hadronic signal have much less corrections Effect on systematic errors Effect on systematic errors 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

71 P. Loch U of Arizona June 30, 2009 Numerical Inversion Transfer of response function from dependence on true variable to dependence on measured variable 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

72 P. Loch U of Arizona June 30, 2009 ATLAS Cell Weights Cell signal weighting Cell signal weighting Statistically determined cell signal Statistically determined cell signal weights try to compensate for weights try to compensate for e/h>1 in jet context e/h>1 in jet context Motivated by H1 cell weighting Motivated by H1 cell weighting High cell signal density indicates High cell signal density indicates on average electromagnetic on average electromagnetic signal origin signal origin Ideally weight = 1 Low cell signal indicates hadronic deposit Low cell signal indicates hadronic deposit Weight > 1 Cell weights are determined as function of cell signal density Cell weights are determined as function of cell signal density Use truth jet matching in fully simulated QCD di-jet events Use truth jet matching in fully simulated QCD di-jet events Crack regions not included in fit Crack regions not included in fit Residual jet energy scale corrections – see next slides Residual jet energy scale corrections – see next slides 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

73 P. Loch U of Arizona June 30, 2009 Cell Weight Calibration For Jets Cell signal weighting functions do not restore jet energy scale for all jets Cell signal weighting functions do not restore jet energy scale for all jets Crack regions not included in fits Crack regions not included in fits Only on jet context used for fitting weights Only on jet context used for fitting weights Cone jets with R=0.7 Cone jets with R=0.7 Only one calorimeter signal definition used for weight fits Only one calorimeter signal definition used for weight fits CaloTowers CaloTowers Additional response corrections applied to restore linearity Additional response corrections applied to restore linearity Non-optimal resolution for other than reference jet samples can be expected Non-optimal resolution for other than reference jet samples can be expected Changing physics environment not explicitly corrected Changing physics environment not explicitly corrected Absolute precision limitation Absolute precision limitation 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

74 P. Loch U of Arizona June 30, 2009 ATLAS Cell Weight Calibration For Jets Cell signal weighting functions do not restore jet energy scale for all jets Cell signal weighting functions do not restore jet energy scale for all jets Crack regions not included in fits Crack regions not included in fits Only on jet context used for fitting weights Only on jet context used for fitting weights Cone jets with R=0.7 Cone jets with R=0.7 Only one calorimeter signal definition used for weight fits Only one calorimeter signal definition used for weight fits CaloTowers CaloTowers Additional response corrections applied to restore linearity Additional response corrections applied to restore linearity Non-optimal resolution for other than reference jet samples can be expected Non-optimal resolution for other than reference jet samples can be expected Changing physics environment not explicitly corrected Changing physics environment not explicitly corrected Absolute precision limitation Absolute precision limitation 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

75 P. Loch U of Arizona June 30, 2009 ATLAS Cell Weight Calibration For Jets Cell signal weighting functions do not restore jet energy scale for all jets Cell signal weighting functions do not restore jet energy scale for all jets Crack regions not included in fits Crack regions not included in fits Only one jet context used for fitting weights Only one jet context used for fitting weights Cone jets with R=0.7 Cone jets with R=0.7 Only one calorimeter signal definition used for weight fits Only one calorimeter signal definition used for weight fits CaloTowers CaloTowers Additional response corrections applied to restore linearity Additional response corrections applied to restore linearity Non-optimal resolution for other than reference jet samples can be expected Non-optimal resolution for other than reference jet samples can be expected Changing physics environment not explicitly corrected Changing physics environment not explicitly corrected Absolute precision limitation Absolute precision limitation Response for jets in ttbar (same jet finder as used for determination of calibration functions with QCD events) CERN-OPEN-2008- 020 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

76 P. Loch U of Arizona June 30, 2009 Final Jet Energy Scale Calibration Jet energy scale (JES) for first data Jet energy scale (JES) for first data Fully Monte Carlo based calibrations hard to validate quickly with initial data Fully Monte Carlo based calibrations hard to validate quickly with initial data Too many things have to be right, including underlying event tunes, pile-up activity, etc. Too many things have to be right, including underlying event tunes, pile-up activity, etc. Mostly a generator issue in the beginning Mostly a generator issue in the beginning Need flat response and decent energy resolution for jets as soon as possible Need flat response and decent energy resolution for jets as soon as possible Data driven scenario a la DZero implemented both for ATLAS and CMS Data driven scenario a la DZero implemented both for ATLAS and CMS Additional jet by jet corrections Additional jet by jet corrections Interesting ideas to use all observable signal features for jets to calibrate Interesting ideas to use all observable signal features for jets to calibrate Geometrical moments Geometrical moments Energy sharing in calorimeters Energy sharing in calorimeters Concerns about stability and MC dependence to be understood Concerns about stability and MC dependence to be understood Can consider e.g. truncated moments using only prominent constituents for stable signal Can consider e.g. truncated moments using only prominent constituents for stable signal 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

77 P. Loch U of Arizona June 30, 2009 CMS Factorized Jet Calibration Factorized calibration allows use of collision data Factorized calibration allows use of collision data CMS sequence applies factorized scheme with required and optional corrections CMS sequence applies factorized scheme with required and optional corrections Required corrections can initially be extracted from collision data Required corrections can initially be extracted from collision data Average signal offset from pile-up and UE can be extracted from minimum bias triggers Average signal offset from pile-up and UE can be extracted from minimum bias triggers Relative direction dependence of response can be corrected from di- jet pT balance Relative direction dependence of response can be corrected from di- jet pT balance The absolute pT scale correction can be derived from prompt photon production The absolute pT scale correction can be derived from prompt photon production Optional corrections refine jet calibration Optional corrections refine jet calibration Use jet by jet calorimeter or track observables to reduce fluctuations Use jet by jet calorimeter or track observables to reduce fluctuations Includes energy fractions in EMC, track pT fractions, underlying event corrections using jet areas, flavor dependencies and others… Includes energy fractions in EMC, track pT fractions, underlying event corrections using jet areas, flavor dependencies and others… May need very good simulations! May need very good simulations! 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

78 P. Loch U of Arizona June 30, 2009 ATLAS JES Correction Model for First Data optional data driven MC 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

79 P. Loch U of Arizona June 30, 2009 Data Driven JES Corrections (1) PileUp subtraction PileUp subtraction Goal: Goal: Correct in-time and residual out-of-time pile-up contribution to a jet on average Correct in-time and residual out-of-time pile-up contribution to a jet on average Tools: Tools: Zero bias (random) events, minimum bias events Zero bias (random) events, minimum bias events Measurement: Measurement: Et density in Δ  ×Δφ bins as function of Et density in Δ  ×Δφ bins as function of # vertices # vertices TopoCluster feature (size, average TopoCluster feature (size, average energy as function of depth) changes energy as function of depth) changes as function of # vertices as function of # vertices Remarks: Remarks: Uses expectations from the average Et flow for a given instantaneous luminosity Uses expectations from the average Et flow for a given instantaneous luminosity Instantaneous luminosity is measured by the # vertices in the event Instantaneous luminosity is measured by the # vertices in the event Requires measure of jet size (AntiKt advantage) Requires measure of jet size (AntiKt advantage) Concerns: Concerns: Stable and safe determination of average Stable and safe determination of average 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c Determination of the Absolute Jet Energy Scale in the D0 Calorimeters. NIM A424, 352 (1999) Note that magnitude of correction depends on calorimeter signal processing!

80 P. Loch U of Arizona June 30, 2009 Data Driven JES Corrections (2) Absolute response Absolute response Goal: Goal: Correct for energy (pT) dependent jet response Correct for energy (pT) dependent jet response Tools: Tools: Direct photons, Z+jet(s),… Direct photons, Z+jet(s),… Measurement: Measurement: pT balance of well calibrated system (photon, Z) pT balance of well calibrated system (photon, Z) against jet in central region against jet in central region Remarks: Remarks: Usually uses central reference and central jets (region of flat reponse) Usually uses central reference and central jets (region of flat reponse) Concerns: Concerns: Limit in precision and estimates for systematics w/o well understood simulations not clear Limit in precision and estimates for systematics w/o well understood simulations not clear 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

81 P. Loch U of Arizona June 30, 2009 Data Driven JES Corrections (3) Direction response corrections Direction response corrections Goal: Goal: Equalize response as function of jet Equalize response as function of jet (pseudo)rapidity (pseudo)rapidity Tools: Tools: QCD di-jets QCD di-jets Direct photons Direct photons Measurement: Measurement: Di-jet pT balance uses Di-jet pT balance uses reference jet in well calibrated reference jet in well calibrated (central) region to correct (central) region to correct second jet further away second jet further away Measure hadronic response Measure hadronic response variations as function of the jet variations as function of the jet direction with the missing Et direction with the missing Et projection fraction (MPF) method projection fraction (MPF) method Remarks: Remarks: MPF only needs jet for direction reference MPF only needs jet for direction reference Bi-sector in di-jet balance explores different sensitivities Bi-sector in di-jet balance explores different sensitivities Concerns: Concerns: MC quality for systematic uncertaunty evaluation MC quality for systematic uncertaunty evaluation Very different (jet) energy scales between reference and probed jet Very different (jet) energy scales between reference and probed jet uncalibrated CERN-OPEN-2008- 020 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

82 P. Loch U of Arizona June 30, 2009 4. Jet Reconstruction Performance 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

83 P. Loch U of Arizona June 30, 2009 Jet Performance Evaluations (1) Jet performance evaluation Jet performance evaluation Proof of success for each method Proof of success for each method Closure tests applied to calibration data source Closure tests applied to calibration data source Strong indications that one jet reconstruction approach is not sufficient Strong indications that one jet reconstruction approach is not sufficient Evaluation needs to be extended to different final states Evaluation needs to be extended to different final states Systematic errors and corrections for alternative jet finders and configurations need to be evaluated Systematic errors and corrections for alternative jet finders and configurations need to be evaluated 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c G. Salam, talk at ATLAS Hadronic Calibration Workshop, Tucson, Arizona, USA, March 2009

84 P. Loch U of Arizona June 30, 2009 Jet Performance Evaluations (2) Jet signal linearity and resolution Jet signal linearity and resolution Closure tests for calibration determination Closure tests for calibration determination Ultimate precision and resolution for given method applied to calibration sample Ultimate precision and resolution for given method applied to calibration sample Response comparisons for different signal definitions Response comparisons for different signal definitions Need to reduce exploration phase space Need to reduce exploration phase space Real data needed for final decision Real data needed for final decision Effect of calibration on inclusive jet cross-section Effect of calibration on inclusive jet cross-section One the first physics results expected from ATLAS & CMS One the first physics results expected from ATLAS & CMS CMS PAS PFT-09-001 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

85 P. Loch U of Arizona June 30, 2009 Jet Performance Evaluations (3) Jet signal linearity and resolution Jet signal linearity and resolution Closure tests for calibration determination Closure tests for calibration determination Ultimate precision and resolution for given method applied to calibration sample Ultimate precision and resolution for given method applied to calibration sample Response comparisons for different signal definitions Response comparisons for different signal definitions Need to reduce exploration phase space Need to reduce exploration phase space Real data needed for final decision Real data needed for final decision Effect of calibration on inclusive jet cross-section Effect of calibration on inclusive jet cross-section One the first physics results expected from ATLAS & CMS One the first physics results expected from ATLAS & CMS CMS PAS SBM-07-001 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

86 P. Loch U of Arizona June 30, 2009 Data Driven Evaluation Photon+jet(s) Photon+jet(s) Well measured electromagnetic system balances jet response Well measured electromagnetic system balances jet response Central value theoretical uncertainty ~2% limits precision Central value theoretical uncertainty ~2% limits precision Due to photon isolation requirements But very good final state for evaluating calibrations But very good final state for evaluating calibrations Can test different correction levels in factorized calibrations Can test different correction levels in factorized calibrations E.g., local hadronic calibration in ATLAS E.g., local hadronic calibration in ATLAS Limited pT reach for 1-2% precision Limited pT reach for 1-2% precision 25->300 GeV within 100 pb -1 25->300 GeV within 100 pb -1 Z+jet(s) Z+jet(s) Similar idea, but less initial statistics Similar idea, but less initial statistics Smaller reach but less background Smaller reach but less background 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c CERN-OPEN-2008- 020

87 P. Loch U of Arizona June 30, 2009 Data Driven Evaluation Photon+jet(s) Photon+jet(s) Well measured electromagnetic system balances jet response Well measured electromagnetic system balances jet response Central value theoretical uncertainty ~2% limits precision Central value theoretical uncertainty ~2% limits precision Due to photon isolation requirements But very good final state for evaluating calibrations But very good final state for evaluating calibrations Can test different correction levels in factorized calibrations Can test different correction levels in factorized calibrations E.g., local hadronic calibration in ATLAS E.g., local hadronic calibration in ATLAS Limited pT reach for 1-2% precision Limited pT reach for 1-2% precision 25->300 GeV within 100 pb-1 25->300 GeV within 100 pb-1 Z+jet(s) Z+jet(s) Similar idea, but less initial statistics Similar idea, but less initial statistics Smaller reach but less background Smaller reach but less background 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c CERN-OPEN-2008- 020

88 P. Loch U of Arizona June 30, 2009 W Mass Spectroscopy In-situ calibration validation handle In-situ calibration validation handle Precise reference in ttbar events Precise reference in ttbar events Hadronically decaying W- bosons Hadronically decaying W- bosons Jet calibrations should reproduce W-mass Jet calibrations should reproduce W-mass Note color singlet source Note color singlet source No color connection to rest of collision – different underlying event as QCD No color connection to rest of collision – different underlying event as QCD Also only light quark jet reference Also only light quark jet reference Expected to be sensitive to jet algorithms Expected to be sensitive to jet algorithms Narrow jets perform better – as expected Narrow jets perform better – as expected 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c CERN-OPEN-2008- 020

89 P. Loch U of Arizona June 30, 2009 Jets Not From Hard Scatter Dangerous background for W+n jets cross- sections etc. Dangerous background for W+n jets cross- sections etc. Lowest pT jet of final state can be faked or misinterpreted as coming from underlying event or multiple interactions Lowest pT jet of final state can be faked or misinterpreted as coming from underlying event or multiple interactions Extra jets from UE are hard to handle Extra jets from UE are hard to handle No real experimental indication of jet source No real experimental indication of jet source Some correlation with hard scattering? Some correlation with hard scattering? Jet area? Jet area? No separate vertex No separate vertex Jet-by-jet handle for multiple proton interactions Jet-by-jet handle for multiple proton interactions Classic indicator for multiple interactions is number of reconstructed vertices in event Classic indicator for multiple interactions is number of reconstructed vertices in event Tevatron with RMS(z_vertex) ~ 30 cm Tevatron with RMS(z_vertex) ~ 30 cm LHC RMS(z_vertex) ~ 8 cm LHC RMS(z_vertex) ~ 8 cm If we can attach vertices to reconstructed jets, we can in principle identify jets not from hard scattering If we can attach vertices to reconstructed jets, we can in principle identify jets not from hard scattering Limited to pseudorapidities within 2.5! Limited to pseudorapidities within 2.5! Track jets Track jets Find jets in recon- Find jets in reconstructed tracks structed tracks ~60% of jet pT, ~60% of jet pT, with RMS ~0.3 – with RMS ~0.3 – not a good not a good kinematic estimator kinematic estimator Dedicated algorithm Dedicated algorithm Cluster track jets in Cluster track jets in pseudo-rapidity, azimuth, pseudo-rapidity, azimuth, and delta(Z Vertex ) and delta(Z Vertex ) Match track and Match track and calorimeter jet calorimeter jet Also helps response! Also helps response! 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c CERN-OPEN-2008- 020

90 P. Loch U of Arizona June 30, 2009 5. Missing Transverse Energy 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

91 P. Loch U of Arizona June 30, 2009 Hard signal in calorimeters Hard signal in calorimeters Fully reconstructed & calibrated particles and jets Fully reconstructed & calibrated particles and jets Not always from hard interaction! Not always from hard interaction! Hard signal in muon spectrometer Hard signal in muon spectrometer Fully reconstructed & calibrated muons Fully reconstructed & calibrated muons May generate isolated or embedded soft calorimeter signals May generate isolated or embedded soft calorimeter signals Care needed to avoid double counting Care needed to avoid double counting Soft signals in calorimeters Soft signals in calorimeters Signals not used in reconstructed physics objects Signals not used in reconstructed physics objects I.e., below reconstruction threshold(s) I.e., below reconstruction threshold(s) Needs to be included in MET to reduce scale biases and improve resolution Needs to be included in MET to reduce scale biases and improve resolution Need to avoid double counting Need to avoid double counting Common object use strategy in ATLAS Common object use strategy in ATLAS Find smallest available calorimeter signal base for physics objects (cells or cell clusters) Find smallest available calorimeter signal base for physics objects (cells or cell clusters) Check for exclusive bases Check for exclusive bases Same signal can only be used in one physics object Same signal can only be used in one physics object Veto MET contribution from already used signals Veto MET contribution from already used signals Track with selected base Track with selected base Priority of association is defined by reconstruction uncertainties Priority of association is defined by reconstruction uncertainties Electrons (highest quality) → photons → muons* → taus → jets (lowest quality) Electrons (highest quality) → photons → muons* → taus → jets (lowest quality) Detector Signal Contribution to MET 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

92 P. Loch U of Arizona June 30, 2009 MET is determined by hard signals in event MET is determined by hard signals in event Reconstructed particles and jets above threshold Reconstructed particles and jets above threshold All objects on well defined energy scale, e.g. best reconstruction for individual object type All objects on well defined energy scale, e.g. best reconstruction for individual object type Really no freedom to change scales for any of these objects Really no freedom to change scales for any of these objects Little calibration to be done for MET Little calibration to be done for MET Note that detector inefficiencies are corrected for physics objects Note that detector inefficiencies are corrected for physics objects Some freedom for soft MET contribution… Some freedom for soft MET contribution… Signals not used in physics objects often lack corresponding context to constrain calibration Signals not used in physics objects often lack corresponding context to constrain calibration ATLAS has developed a low bias “local” calibration for the calorimeters based on signal shapes inside calorimeters ATLAS has developed a low bias “local” calibration for the calorimeters based on signal shapes inside calorimeters Some degree of freedom here Some degree of freedom here But contribution is small and mostly balanced in Et anyway But contribution is small and mostly balanced in Et anyway Source here often UE/pile-up! …and overall acceptance limitations …and overall acceptance limitations Detector “loses” particles in non-instrumented areas or due to magnetic field in inner cavity Detector “loses” particles in non-instrumented areas or due to magnetic field in inner cavity Same remarks as above, very small and likely balanced signals Same remarks as above, very small and likely balanced signals Event topology dependent adjustments to MET are imaginable to recover these losses Event topology dependent adjustments to MET are imaginable to recover these losses I prefer “validation” rather than “calibration” I prefer “validation” rather than “calibration” Discrepancies in MET need to be isolated for systematic control Discrepancies in MET need to be isolated for systematic control 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c Remarks to MET Calibration

93 P. Loch U of Arizona June 30, 2009 MET scale can be checked with physics MET scale can be checked with physics Look for one hadronic and one leptonic tau from Z decays Look for one hadronic and one leptonic tau from Z decays Can be triggered nicely with lepton + MET requirement Can be triggered nicely with lepton + MET requirement Use collinear approximation to reconstruct invariant mass Use collinear approximation to reconstruct invariant mass Massless taus Massless taus Neutrinos assumed to be collinear to observable tau decay products Neutrinos assumed to be collinear to observable tau decay products Check dependence of invariant mass on MET scale variations Check dependence of invariant mass on MET scale variations Expect correlation! Expect correlation! Determined from two reconstructed MET components and directions of detectable decay products CERN-OPEN-2008-020 100 pb-1 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c CERN-OPEN-2008- 020 MET Scale Validation

94 P. Loch U of Arizona June 30, 2009 What is that? What is that? MET contribution from response variations MET contribution from response variations Cracks, azimuthal response variations… Cracks, azimuthal response variations… Never/slowly changing Never/slowly changing Particle dependent Particle dependent MET contribution from mis- calibration MET contribution from mis- calibration E.g., QCD di-jet with one jet under-calibrated E.g., QCD di-jet with one jet under-calibrated Relative effect generates MET pointing to this jet Relative effect generates MET pointing to this jet Dangerous source of MET Dangerous source of MET Disturbs many final states in a different way Disturbs many final states in a different way Can fake new physics Can fake new physics Suppression strategies Suppression strategies Track jets Track jets Energy sharing between calorimeters Energy sharing between calorimeters Event topology analysis Event topology analysis CERN-OPEN-2008-020 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c (modeled material asymmetry) Fake Missing Et

95 P. Loch U of Arizona June 30, 2009 MET Resolution From MC MET resolution MET resolution Driven by calorimeter Driven by calorimeter Expect sqrt(E) Expect sqrt(E) dependence dependence Studied as function of Studied as function of scalar Et scalar Et Systematically Systematically evaluated with MC evaluated with MC No direct experimental No direct experimental access access Minimum bias with limited reach/precision? Minimum bias with limited reach/precision? Concern is pile-up effect on scalar Et Concern is pile-up effect on scalar Et Will discuss experimental access on next slide(s) Will discuss experimental access on next slide(s) CERN-OPEN-2008-020 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

96 P. Loch U of Arizona June 30, 2009 Data Driven MET Scale & Resolution (1) Experimental access Experimental access Use bi-sector signal projections in Z decays Use bi-sector signal projections in Z decays Longitudinal projection sensitive to scale Longitudinal projection sensitive to scale Calibration of hadronic recoil Calibration of hadronic recoil Perpendicular projection sensitive to angular resolution Perpendicular projection sensitive to angular resolution Neutrinofication Neutrinofication Assumed to be very similar in Z and W Assumed to be very similar in Z and W One lepton in Z decay can be “neutrinofied” One lepton in Z decay can be “neutrinofied” Access to MET resolution and scale Access to MET resolution and scale Hadronic recoil contribution Hadronic recoil contribution 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

97 P. Loch U of Arizona June 30, 2009 MET scale MET scale Folds hadronic scale with acceptance Folds hadronic scale with acceptance Note: no jets needed! Note: no jets needed! Experimental tool to validate calibration of “unused” calorimeter signal Experimental tool to validate calibration of “unused” calorimeter signal Hard objects can be removed from recoil Hard objects can be removed from recoil One possible degree of freedom in MET “calibration” One possible degree of freedom in MET “calibration” Relevance for other final states to be evaluated Relevance for other final states to be evaluated Otherwise purely experimental handle! Otherwise purely experimental handle! MET resolution MET resolution Can be measured along perpendicular and longitudinal axis Can be measured along perpendicular and longitudinal axis Resolution scale is scalar Et sum of hadronic calorimeter signal Resolution scale is scalar Et sum of hadronic calorimeter signal Biased by UE and pile-up (MC needed here) Biased by UE and pile-up (MC needed here) Qualitatively follows calorimeter energy resolution Qualitatively follows calorimeter energy resolution CERN-OPEN-2008-020 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c Data Driven MET Scale & Resolution (2)

98 P. Loch U of Arizona June 30, 2009 Missing ET is a complex experimental quantity Missing ET is a complex experimental quantity Sensitive to precision and resolution of hard object reconstruction Sensitive to precision and resolution of hard object reconstruction MET is calibrated by everything MET is calibrated by everything Easily affected by detector problems and inefficiencies Easily affected by detector problems and inefficiencies Careful analysis of full event topology Careful analysis of full event topology Signal shapes in physics and detector Signal shapes in physics and detector Known unknown (1): effect of underlying event Known unknown (1): effect of underlying event Some correlation with hard scattering Some correlation with hard scattering Insignificant contribution?? Insignificant contribution?? To be confirmed early with di-jets To be confirmed early with di-jets Known unknown (2): effect of pile-up Known unknown (2): effect of pile-up Level of activity not so clear Level of activity not so clear Minimum bias first and urgent experimental task Minimum bias first and urgent experimental task Expectation is cancellation on average (at least) Expectation is cancellation on average (at least) Detector signal thresholds/acceptance potentially introduce asymmetries Detector signal thresholds/acceptance potentially introduce asymmetries Need to know the “real” detector Need to know the “real” detector Considerable contribution to MET fluctuations Considerable contribution to MET fluctuations Severe limitation in sensitivity for discovery Severe limitation in sensitivity for discovery 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c Final Remarks on MET

99 P. Loch U of Arizona June 30, 2009 6. Conclusions 1.a 1.b 2.a 2.b 3.a 3.b 3.c 4.a 4.b 4.c 5.a 5.b 5.c

100 P. Loch U of Arizona June 30, 2009 Conclusions This was a mere snapshot This was a mere snapshot Jet reconstruction at LHC deserves a book Jet reconstruction at LHC deserves a book Complex environment, complex signals, lots of information content both in ATLAS and CMS events Complex environment, complex signals, lots of information content both in ATLAS and CMS events Both experiments are on their way Both experiments are on their way Expected performance could be confirmed or exceeded with full simulations in large LHC phase space Expected performance could be confirmed or exceeded with full simulations in large LHC phase space No real problems expected, but data can always bring a few surprises No real problems expected, but data can always bring a few surprises Missing Et performance expectations well developed Missing Et performance expectations well developed Complex quantity likely needs most time to be understood Complex quantity likely needs most time to be understood Collects all systematics from all other physics object reconstruction! Collects all systematics from all other physics object reconstruction! We are waiting for data! We are waiting for data!

101 P. Loch U of Arizona June 30, 2009 The Other Stuff

102 P. Loch U of Arizona June 30, 2009 We expected clusters to represent individual particles We expected clusters to represent individual particles Cannot be perfect in busy jet environment! Cannot be perfect in busy jet environment! Shower overlap in finite calorimeter granularity Shower overlap in finite calorimeter granularity Some resolution power, though Some resolution power, though Much better than for tower jets! Much better than for tower jets! ~1.6:1 particles:clusters in central region ~1.6:1 particles:clusters in central region This is an average estimator subject to large fluctuations This is an average estimator subject to large fluctuations ~1:1 in endcap region ~1:1 in endcap region Best match of readout granularity, shower size and jet particle energy flow Best match of readout granularity, shower size and jet particle energy flow Happy coincidence, not a design feature of the ATLAS calorimeter! Happy coincidence, not a design feature of the ATLAS calorimeter! Jet Composition S.D. Ellis et al., Prog.Part.Nucl.Phys.60:484-551,2008

103 P. Loch U of Arizona June 30, 2009 Jet Shapes (from G. Salam’s talk at the ATLAS Hadronic Calibration Workshop Tucson 2008)

104 P. Loch U of Arizona June 30, 2009 Calibration Flow Lots of work in calorimeter domain!

105 P. Loch U of Arizona June 30, 2009 Sum up electromagnetic scale calorimeter cell signals into towers Sum up electromagnetic scale calorimeter cell signals into towers Fixed grid of Δ η x Δ φ = 0.1 x 0.1 Fixed grid of Δ η x Δ φ = 0.1 x 0.1 Non-discriminatory, no cell suppression Non-discriminatory, no cell suppression Works well with pointing readout geometries Works well with pointing readout geometries Larger cells split their signal between towers according to the overlap area fraction Larger cells split their signal between towers according to the overlap area fraction Tower noise suppression Tower noise suppression Some towers have net negative signals Some towers have net negative signals Apply “nearest neighbour tower recombination” Apply “nearest neighbour tower recombination” Combine negative signal tower(s) with nearby positive signal towers until sum of signals > 0 Combine negative signal tower(s) with nearby positive signal towers until sum of signals > 0 Remove towers with no nearby neighbours Remove towers with no nearby neighbours Towers are “massless” pseudo-particles Towers are “massless” pseudo-particles Find jets Find jets Note: towers have signal on electromagnetic energy scale Note: towers have signal on electromagnetic energy scale Calibrate jets Calibrate jets Retrieve calorimeter cell signals in jet Retrieve calorimeter cell signals in jet Apply signal weighting functions to these signals Apply signal weighting functions to these signals Recalculate jet kinematics using these cell signals Recalculate jet kinematics using these cell signals Note: there are cells with negative signals! Note: there are cells with negative signals! Apply final corrections Apply final corrections CaloTower Jets

106 P. Loch U of Arizona June 30, 2009 TopoCluster Jets

107 P. Loch U of Arizona June 30, 2009 Apply noise suppression to tower jets Apply noise suppression to tower jets Topological clustering is used as a noise suppression tool only Topological clustering is used as a noise suppression tool only Similar to DZero approach Similar to DZero approach New implementation New implementation Only in ESD context so far Only in ESD context so far Working on schema to bring these jets into the AOD Working on schema to bring these jets into the AOD Including constituents Including constituents Allows comparisons for tower and cluster jets with similar noise contribution Allows comparisons for tower and cluster jets with similar noise contribution Should produce rather similar jets than tower jets at better resolution Should produce rather similar jets than tower jets at better resolution Less towers per jet Less towers per jet TopoTower Jets CERN-OPEN-2008-020

108 P. Loch U of Arizona June 30, 2009 MET Calorimeter Issues Calorimeter issues Calorimeter issues About 70-90% of all cells have no About 70-90% of all cells have no true or significant signal true or significant signal Depending on final state, of course Depending on final state, of course Applying symmetric or asymmetric Applying symmetric or asymmetric noise cuts to cell signals noise cuts to cell signals Reduces fluctuations significantly Reduces fluctuations significantly But introduces a bias (shift in But introduces a bias (shift in average missing Et) average missing Et) Topological clustering applies more reasonable noise cut Topological clustering applies more reasonable noise cut Cells with very small signals can survive based on the signals in neighboring cells Cells with very small signals can survive based on the signals in neighboring cells Still small bias possible but close-to-ideal suppression of noise Still small bias possible but close-to-ideal suppression of noise K. Cranmer, in talk by S. Menke, ATLAS Physics Workshop 07/2005

109 P. Loch U of Arizona June 30, 2009 “EM” TopoCluster Sliding Window Cluster Hadronic TopoCluster Cluster Overlap

110 P. Loch U of Arizona June 30, 2009 Variables For Cluster Overlap Resolution TopoClusters and Sliding Window clusters representing the same em shower are different TopoClusters and Sliding Window clusters representing the same em shower are different Different shapes Different shapes TopoCluster size determined by cell signal topology TopoCluster size determined by cell signal topology SW cluster size fixed by client SW cluster size fixed by client Both clusters will have different cell content! Both clusters will have different cell content! Different signal fluctuations Different signal fluctuations No noise suppression in SW clusters No noise suppression in SW clusters But less direct contributions from small signal “marginal” cells Noise suppression in TopoClusters Noise suppression in TopoClusters Potentially more small signal cells with relatively large fluctuations Possible variables to measure overlap (under study) Possible variables to measure overlap (under study) Total raw signal Total raw signal Affected by different noise characteristics Affected by different noise characteristics Relative signal distribution in sampling layers Relative signal distribution in sampling layers Could be better as some noise is unfolded in the ratios Could be better as some noise is unfolded in the ratios Geometrical distance (barycenter-to-barycenter in 3-d) Geometrical distance (barycenter-to-barycenter in 3-d) Not available for SW (could easily be implemented!) Not available for SW (could easily be implemented!) Subject to detector granularity changes (?) Subject to detector granularity changes (?) Measure common cell content Measure common cell content Similar motivation as for ESD Similar motivation as for ESD Adds several other (more stable) measures to overlap resolution, see next slides Adds several other (more stable) measures to overlap resolution, see next slides

111 P. Loch U of Arizona June 30, 2009 TopoClusters have 3-d geometry TopoClusters have 3-d geometry Barycenter (x,y,z) Barycenter (x,y,z) In AOD, from (R,η,φ) In AOD, from (R,η,φ) Extension along and perpendicular to “direction of flight” Extension along and perpendicular to “direction of flight” Measured by 2 nd geometrical moments Measured by 2 nd geometrical moments Vertex assumption (0,0,0) for Vertex assumption (0,0,0) for right now right now Principal axis available for large enough clusters Can calculate envelop around barycenter Can calculate envelop around barycenter Presently ellipsoidal Presently ellipsoidal Could include apparent “longitudinal asymmetry” of em showers Use simple model of Use simple model of longitudinal profile longitudinal profile Geometrical Features Of Clusters

112 P. Loch U of Arizona June 30, 2009 AOD TopoClusters have no cells AOD TopoClusters have no cells Need to come up with some geometrical measure Need to come up with some geometrical measure Use the envelop! Use the envelop! Can calculate likelihood that cell is within envelop Can calculate likelihood that cell is within envelop Introduces two parameters with typical values: Introduces two parameters with typical values: May need some tuning! May need some tuning! Define cell i is inside topo cluster c when… Define cell i is inside topo cluster c when… Associating Cells With TopoClusters

113 P. Loch U of Arizona June 30, 2009 Cell Content Variables Shared cells between SW and TopoCluster provide: Shared cells between SW and TopoCluster provide: Fractional number of cells shared Fractional number of cells shared Can be calculated for both clusters Can be calculated for both clusters Likely more useful for TopoCluster Energy density measure Energy density measure Fraction of cluster signal in shared cells Fraction of cluster signal in shared cells Raw signal reference Relative profiles Relative profiles Re-summation of raw sampling energies allows to calculate fractions by sampling for both types of clusters Re-summation of raw sampling energies allows to calculate fractions by sampling for both types of clusters … When used with muons: When used with muons: Fraction of cells associated with a muon inside a TopoCluster Fraction of cells associated with a muon inside a TopoCluster Discard TopoCluster signal in MET calculation if muon corrected for calo energy loss Discard TopoCluster signal in MET calculation if muon corrected for calo energy loss

Detector Understanding with First LHC Data DESY, Hamburg, 29 June – 3 July 2009 Introduction To Jet & Missing Transverse Energy Reconstruction at LHC Peter.

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Detector Understanding with First LHC Data DESY, Hamburg, 29 June – 3 July 2009 Introduction To Jet & Missing Transverse Energy Reconstruction at LHC Peter.

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Presentation on theme: "Detector Understanding with First LHC Data DESY, Hamburg, 29 June – 3 July 2009 Introduction To Jet & Missing Transverse Energy Reconstruction at LHC Peter."— Presentation transcript:

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